• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.5
• /
• pp.269-277
• /
• 2000

We propose two new types multi-stage blind clustering equalizers for QAM demoulation, which are called a complex classification algorithm(CCA) and a radial basis function algorithm(RBFA). The CCA uses a clustering technique based on the joint gaussian probability function and computes separately the real part and imaginary part for simple implementation as well as less computation. In order to improve the performance of CCA, the Dual-Mode CCA(DMCCA) incorporates the CCA tap-updating mode with the decision-directed(DD) mode. The RBFA reduces the number of cluster centers through three steps using the classification technique of RBF and then updates the equalizer taps for QAM demodulation. Test results on 16-QAM confirm that the proposed algorithms perform better the conventional multi-state equalizers in the senses of SER and MSE under multi-path fading channel.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.5 no.2
• /
• pp.16-24
• /
• 2002

Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform. The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.

• Natural Product Sciences
• /
• v.24 no.3
• /
• pp.164-170
• /
• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.12 no.10
• /
• pp.799-807
• /
• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• The Journal of the Korea Contents Association
• /
• v.1 no.1
• /
• pp.8-14
• /
• 2001

In this paper, we suggests a algorithms of recognizing the disease region by extracting particular organ from medical image. This method can extract liver region in spite of input image including many organs and charged format by using multi-threshold of feed-back-structure for segmentation liver region, and suggest the recognition of disease region in extracted liver, using multi-neural network structured by RBF and BP, overcoming the defect of single-neural network. The algorithm in this paper is proficient in adaptation for a multi form change of input medical image. This algorithm can be used at tole-medicine through automatic recognition after recognizing of the disease region by real-tire medical Image.

• Journal of the Korean Society of Visualization
• /
• v.16 no.2
• /
• pp.23-30
• /
• 2018

The present study compares flow fields reconstructed by data assimilation method with different combinations of parameters. As a data assimilation method, Vortex-in-Cell-sharp (VIC#), which supplements additional constraints and multigrid approximation to Vortex-in-Cell-plus (VIC+), is used to reconstruct flow fields from scattered particle tracks. Two parameters, standard deviation of Gaussian radial basis function (RBF) and grid spacing, are mainly tested using artificial data sets which contain few particle tracks. Consequent flow fields are analyzed in terms of flow structure sizes. It is demonstrated that sizes of the flow structures are proportional to an actual scale of the standard deviation of RBF. It implies that a combination of larger grid spacing and smaller standard deviation which preserves the actual standard deviation is able to save computational resources in case of a low track density. In addition, a simple comparison using an experimental data filled with dense particle tracks is conducted.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.595-601
• /
• 2011

This paper presents balancing control of inverted pendulum on the ROBOKER arm using visual information. The angle of the inverted pendulum placed on the robot arm is detected by a stereo camera and the detected angle is used as a feedback and tracking error for the controller. Thus, the overall closed loop forms a visual servoing control task. To improve control performance, neural network is introduced to compensate for uncertainties. The learning algorithm of radial basis function(RBF) network is performed by the digital signal controller which is designed to calculate floating format data and embedded on a field programmable gate array(FPGA) chip. Experimental studies are conducted to confirm the performance of the overall system implementation.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.16 no.4
• /
• pp.436-442
• /
• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.5
• /
• pp.652-658
• /
• 2010

This paper presents the implementation and control of a two wheeled mobile robot(TWMR) based on a balancing mechanism. The TWMR is a mobile inverted pendulum structure that combines an inverted pendulum system and a mobile robot system with two arms instead of a rod. To improve robustness due to disturbances, the radial basis function (RBF) network is used to control an angle and a position at the same time. The reference compensation technique(RCT) is used as a neural control method. Experimental studies are conducted to demonstrate performance of neural network controllers. The robot are implemented with the remote control capability.

• Journal of Sensor Science and Technology
• /
• v.13 no.1
• /
• pp.41-46
• /
• 2004

There is currently much interest in the development of instruments that emulate the senses of humans. Increasingly, there is demand for mimicking the human sense of smell, which is a sophisticated chemosensory system. An electronic nose system is applicable to a large area of industries including environmental monitoring. We have designed a protable electronic nose system using an array of commercial chemical gas sensors for recognizing and analyzing the various odours. In this paper, we have implemented a portable electronic nose system using an array of gas sensors for recognizing and analyzing VOCs (Volatile Organic Compounds) in the field. The accuracy of a portable electronic nose system may be lower than an instrument such as GC/MS (Gas Chromatography/Mass Spectrometer). However, a portable electronic nose system could be used on the field and showed fast response to pollutants in the field. Several different algorithms for odours recognition were used such as BP (Back-Propagation) or LM-BP (Levenberq-Marquardt Back-Propagation). We applied RBF (Radial Basis Function) Network for recognition and quantifying of odours, which has simpler and faster compared to the previously used algorithms such as BP and LM-BP.